Devon Energy Uses Real-time Visibility to Improve Trucking Operations

by ·
9 Aug, 18

Like many other energy companies, Devon Energy, a leading independent oil and natural gas exploration and production company in North America, generates huge volumes of data. The company’s SCADA system monitors 6.5 million data points from multiple sites, with more than 10,000 updates per second. Devon Energy has implemented advanced data historian and analytics technologies to transform all this real-time visibility into actionable insights.

Don Morrison, Real Time Data Engineer at Devon Energy, shared his experience working with newer real-time data and analytics technologies at the ARC Industry Forum. Devon Energy is based in Oklahoma City with on-shore operations in the US and Canada. The company’s 2017 energy portfolio consisted of 46 percent oil, 37 percent natural gas, and 17 percent natural gas liquids (NGL).

It has a large inventory of future projects planned, mostly in western Oklahoma, southeastern New Mexico, and the Delaware basin. To continue its successful track record as an industry leader in technology, the company has nurtured a culture of innovation. This includes establishing groups to implement innovative new technologies that provide business value.

Aligning Information Technology and Operations Technology Mr. Morrison is part of the real-time systems OT data and analytics group responsible for managing the data from the company’s SCADA and enterprise historian systems.

The group works closely with IT to ensure that the products and solutions align with the IT strategy. Devon Energy monitors around 6.5 million data points in the SCADA system, with more than 10,000 updates per second. The update times range from around one second to 15 minutes, depending on the asset.

All data is ported from the SCADA system and stored in an enterprise historian system. The data is used by both the company’s operations and business groups; including completions, drilling, and production using the Seeq application for a user-driven analytics experience.

Improving Liquid Tank Haul-off Operations

At Devon Energy’s unmanned, remote production sites, the liquid haul-off process used to involve a significant amount of manual data entry. This surprisingly complex operation involves custody transfer of oil and water from the production facility to a tanker truck to move the product to a point of sale for marketing purposes.

Oil, water, and gas components are stored in different tank batteries. A tank battery is a storage tank used to store crude oil and components from a well at the drilling site. Ideally, trucks are dispatched to site when the tanks approach capacity, but not any sooner.

That’s because Devon pays to transport a full load whether the truck is full or not. On the other hand, if the tanks are not emptied in time, the company would have to halt production, which would be even more costly. In the past, the truck drivers would simply write the amount of oil or water taken and put it in a mailbox hidden somewhere on the site.

Various employees around the company then manually entered this information into spreadsheets. As a result, the company could never be sure that what the trucker wrote down was accurate or if there was spillage, which was a problem for marketing. The central operations group had no idea what the contracted truck drivers were doing, or even when they were on site, which posed an operational and safety risk.

The entire process was slow, time-consuming, and error prone.

Intelligence Required

Specifically, when a truck was on site, the oil marketing and water management business groups wanted to know:

Did the driver pick up oil or water?

How much oil or water was taken?

Was there more than one truck on site?

Did they receive a full or partial load?

At what rate are the tanks filling up?

Can we predict when the next load will need to occur to be able to schedule pickups more efficiently?

Can we get enough data to rate our service providers?

What is the current level of the tank battery?

Real-time Visibility and Predictive Analytics

To improve real-time visualization into these tank haul-off events, Mr. Morrison’s team developed digital dashboards using tank level information from the SCADA system to enable operators to see when haul-off events occurred, determine how much oil or water was taken, and detect any spills or other issues in real time. The dashboards also showed when the tanks were filling up so the remotely located operators could dispatch trucks in time to avoid having to shut down production.

But this just scratched the surface of the potential functionality. Now that they could visualize the haul-off events, the company wanted to be able to predict when future haul-off events would be required, with enough lead time to optimize the process from both the operational and marketing perspectives.

To reduce the time and effort needed to develop this solution, the company implemented the Seeq predictive analytics tool. Data preparation and cleansing can absorb as much as 80 percent of the development time and costs for this type of project. But with Seeq, the engineer applies a smoothing algorithm that quickly cleans up the data and removes jagged data edges.

Mr. Morrison pointed out that the tool is easy to use and does not require any programming, enabling the engineer to analyze data or find events quickly. “Using the Seeq tool, we can now determine exactly how much oil was taken during each event by matching up the time series data to when the haul-off event started and ended. This enables us to accurately and quickly estimate events using an interactive inventory and haul-off analytics approach built into the tool.”

“Using Seeq to take the time series data directly from the historian enables us to match the data to our assets and analyze the data quickly and accurately.” As Mr. Morrison explained, Devon’s process tests the data in real time, takes the data and runs it through the tool to get an output to their assets, and applies the technology to the company’s approximate 1,200 battery storage units. Using the Seeq API technology, they process the data, write the data back into the historian system, then make the intelligence available for the operational dashboards.

New Tools for Haul-off Reporting and Scheduling

Prior to implementing this data and analytics tool, the company would send out an Excel file for everyone involved in the tank haul-off process to fill in.

The data would then be compiled manually into one file to determine the haul-off schedule.

Now, in addition to automating this data entry, the company can also sync the data to Microsoft Power BI, enabling business users to further “slice and dice” the data in a business reporting format to simplify and improve the accuracy of its haul-off scheduling.